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Updated: Dec 18, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Feature-Based Diversity Optimization for Problem Instance Classification
Wanru Gao1, Samadhi Nallaperuma2, Frank Neumann3
1School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China kelly_gwr@hotmail.com.
Abstract:
Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.
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